Linguistic research benefits from the wide range of resources and software tools developed for natural language processing (NLP) tasks. However, NLP has a strong historical bias towards written language, thereby making these resources and tools often inadequate to address research questions related to the linguistic patterns of spontaneous speech. In this preliminary study, we investigate whether corpora of movie and TV subtitles can be employed to estimate data-driven NLP models adapted to conversational speech. In particular, the presented work explore lexical and syntactic distributional aspects across three genres (conversational, written and subtitles) and three languages (French, English and Taiwan Mandarin). Ongoing work focuses on comparing these three genres on the basis of deeper syntactic conversational patterns , using graph-based modelling and visualisation.